Nutrient and Carbon Export From a Tidewater Glacier to the Coastal Ocean in the Canadian Arctic Archipelago
Bibliographic record
Abstract
Abstract A range of glacially driven processes modify and export freshwater and sediments to the ocean. This glacial runoff may influence biological productivity in coastal ecosystems by supplying essential nutrients and labile carbon. Previous studies of glacial meltwater export to the ocean have primarily been conducted on rivers draining land‐terminating glaciers, or in fjords with large tidewater glaciers. These studies speculate about downstream effects (river studies) or upstream causes (fjord studies) of differing carbon and nutrient availability and biological productivity, but do not measure them. Here, we conduct the first ice‐to‐ocean study at a marine‐terminating glacier in the Canadian Arctic Archipelago (CAA). We characterize the nutrient and carbon content of ice and meltwater collected on the glacier surface, at its margins, and in the near‐shore coastal ocean, all within 1 to 25 km of the glacier terminus. Results demonstrate that while meltwater from a shallow tidewater glacier did not directly increase downstream carbon and nutrient concentrations, it induced upwelling of deeper nutrient‐rich marine water. Although carbon concentrations in meltwater were low, results show that this carbon is potentially more bioavailable than marine carbon. Glacially mediated delivery of labile carbon and upwelling of nutrient‐rich water occurs in summer, when surface waters are nutrient‐limited. Collectively, these processes could benefit surface marine plankton, potentially stimulating production at the base of the food web. Shallow tidewater glaciers are commonly retreating in Arctic regions like the CAA and Svalbard, and understanding how increased meltwater output from these systems impacts marine ecosystems is critical.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".